In stock prediction problems, deep ensemble models are better adapted to dynamically changing stock market environments compared to single time-series networks. However, the existing ensemble models often underutilize real-time market feedback for effective supervision, and base models are pre-trained and fixed in their optimization, which makes them lack adaptability for evolving market environments. To address this issue, we propose a deep-reinforcement-learning-based dynamic ensemble model for stock prediction (DRL-DEM). Firstly, we employ deep reinforcement learning to optimize the weights of deep-learning-based time-series models. Secondly, existing deep-reinforcement-learning methods only consider environmental rewards. Thus we improve the reward function by introducing real-time investment returns as additional feedback signals for the deep-reinforcement-learning algorithm. Finally, an alternating iterative algorithm is used to simultaneously train the base predictors and the deep-reinforcement-learning model, allowing DRL-DEM to fully utilize the supervised information for global coordinated optimization. The experimental results show that in SSE 50 and NASDAQ 100 datasets, the mean square error (MSE) of the proposed method reached 0.011 and 0.005, the Sharpe ratio (SR) reached 2.20 and 1.53, and the cumulative return (CR) reached 1.38 and 1.21. Compared with the best results in the recent model, MSE decreased by 21.4% and 28.6%, SR increased by 81.8% and 82.1%, and CR increased by 89.0% and 89.1%, with higher forecasting accuracy and stronger investment return capability.

Predicting stock trends has garnered extensive attention from investors and researchers due to its potential to optimize stock investment returns. The fluctuation of stock prices is complex and influenced by multiple factors, presenting two major challenges: the first challenge lies in the the temporal dependence of individual stocks and the spatial correlation among multiple stocks. The second challenge emerges from having insufficient historical data availability for newly listed stocks. To address these challenges, this paper proposes a spatio-temporal hypergraph transformer (STHformer). The proposed model employs a temporal encoder with an aggregation module to capture temporal patterns, utilizes self-attention to dynamically generate hyperedges, and selects cross-attention to implement hypergraph-associated convolution. Furthermore, pretraining based on reconstruction of masked sequences is implemented. This framework enhances the model’s cold-start capability, making it more adaptable to newly listed stocks with insufficient training data. Experimental results show that the proposed model, after pretraining on data from over two thousand stocks, performed well on datasets from the stock markets of the United States and China.

With the rapidly growing number of images over the Internet, efficient scalable semantic image retrieval becomes increasingly important. This paper presents a novel approach for semantic image retrieval by combining Convolutional Neural Network (CNN) and Markov Random Field (MRF). As a key step, image concept detection, that is, automatically recognizing multiple semantic concepts in an unlabeled image, plays an important role in semantic image retrieval. Unlike previous work that uses single-concept classifiers one by one, we detect semantic multiconcept by using a multiconcept scene classifier. In other words, our approach takes multiple concepts as a holistic scene for multiconcept scene learning. Specifically, we first train a CNN as a concept classifier, which further includes two types of classifiers: a single-concept fully connected classifier that is best suited to single-concept detection and a multiconcept scene fully connected classifier that is good for holistic scene detection. Then we propose an MRF-based late fusion approach that is able to effectively learn the semantic correlation between the single-concept classifier and multiconcept scene classifier. Finally, the semantic correlation among the subconcepts of images is cought to further improve detection precision. In order to investigate the feasibility and effectiveness of our proposed approach, we conduct comprehensive experiments on two publicly available image databases. The results show that our proposed approach outperforms several state-of-the-art approaches.